The main strengths of Flair come from the use of stacking embeddings, that consist of two types of embeddings: pre-trained GloVe embedding [1]} and contextual string embeddings based on pre-trained language models. [2]} show that the use of pre-trained GloVe embedding increases average \(F_1\)  score by \(1.1\) , and the use of contextual string embeddings brings even larger improvements, around \(4.5\)  absolute \(F_1\)  score on the English NER dataset. However, both of these two types of embeddings are pre-trained on generic data. For example, GloVe embeddings are pre-trained on the English Wikipedia and Gigaword dataset (archive of news stories). These generic data usually have very different characteristics from the biomedical data.
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